Spaces:
Running
Running
| title: Mineral Identifier | |
| emoji: 🪨 | |
| colorFrom: indigo | |
| colorTo: purple | |
| sdk: gradio | |
| sdk_version: 5.29.1 | |
| python_version: 3.11 | |
| app_file: app.py | |
| fullWidth: true | |
| header: default | |
| short_description: Upload a rock image to identify it! | |
| tags: | |
| - geology | |
| - mineralogy | |
| - image-classification | |
| - gradio | |
| - computer-vision | |
| datasets: | |
| - Nech-C/mineralimage5K-98 | |
| pinned: true | |
| # 🪨 Mineral Identifier | |
| Welcome to the **Mineral Identifier** app! This tool uses a deep learning model to identify the **type of mineral** in a rock image you upload. | |
| ## 🚀 Features | |
| - 🔍 **Image classification** powered by a trained neural network | |
| - 📸 Upload an image of a mineral sample | |
| - 💡 Get a **prediction** along with confidence levels | |
| - 🌐 Built with [Gradio](https://gradio.app/) for fast, accessible user interaction | |
| ## 🧠 Behind the Model | |
| The app is powered by a convolutional neural network trained on a curated dataset of mineral images including: | |
| - Quartz | |
| - Calcite | |
| - Feldspar | |
| - Mica | |
| - And more! | |
| If you’d like to explore the dataset used: | |
| - [Dataset on Hugging Face Hub](https://huggingface.co/datasets/Nech-C/mineralimage5K-98) | |
| ## 🛠️ How to Use | |
| 1. Choose a photo of your rock/mineral sample. | |
| 2. The app will process the image and output the **predicted mineral type**. | |
| ## 💬 Feedback | |
| If you encounter any issues or have suggestions for improvements, feel free to open an [issue on GitHub](https://github.com/Nech-C/rockognize/issues) or reach out on the [Hugging Face community](https://huggingface.co/spaces/Nech-C/Rock-Identifier). | |